Introduction
- Presentation of data
- Data wrangling
Materials and Methods
- Data visualization
- Logistic regression
- Principal Component Analysis
- K-means clustering
11th May 2021
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Outline
Introduction
Introduction
Introduction
- Byar & Greene prostate cancer data, from Andrews DF and Herzberg AM (1985)
- Compare four different treatments
- 502 observations of 18 variables
- 27 NA values
Introduction
Variables in the data set
Presentation of the variables with a R output, table, or image?
Data wrangling
Raw data -> Clean data
- Exclude dtime, sdate and sg
- Renaming
Clean data -> Augment data
- Add five new variables: outcome, treatment_mg, EKG_lvl, performance_lvl, age_group
Raw data -> Clean data
- Exclude dtime, sdate and sg
- Renaming
Clean data -> Augment data
- Add five new variables: outcome, treatment_mg, EKG_lvl, performance_lvl, age_group
Materials and Methods
Data visualization
Pre-treatment variables - Numeric
Data visualization
Pre-treatment variables - Categorical
Data visualization
Pre-treatment variables - Heatmap
Data visualization
Treatment, outcome and age
Data visualization
Pre-treatment variables - Categorical
Data visualization
Pre-treatment variables - Heatmap
Data visualization
Treatment, outcome and age
Logistic regression
Logistic regression
Model outcome as function of treatment
Output:
log_mod_treatment
## # A tibble: 4 x 5 ## term estimate std.error statistic p.value ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 (Intercept) 1.11 0.211 5.27 0.000000136 ## 2 treatment_mg0.2 0.0907 0.301 0.301 0.763 ## 3 treatment_mg1 -0.807 0.280 -2.88 0.00394 ## 4 treatment_mg5 -0.0536 0.294 -0.182 0.855
Logistic regression for each variable
Treatment 1.0 mg
Logistic regression
Effects of significant variables for treatment 1.0 mg
Logistic regression
Distribution of significant variables for each outcome
Output:
<<<<<<< HEADlog_mod_treatment
## # A tibble: 4 x 5 ## term estimate std.error statistic p.value ## <chr> <dbl> <dbl> <dbl> <dbl> ## 1 (Intercept) 1.11 0.211 5.27 0.000000136 ## 2 treatment_mg0.2 0.0907 0.301 0.301 0.763 ## 3 treatment_mg1 -0.807 0.280 -2.88 0.00394 ## 4 treatment_mg5 -0.0536 0.294 -0.182 0.855
Logistic regression for each variable
Treatment 1.0 mg
Logistic regression
Effects of significant variables for treatment 1.0 mg
Logistic regression
Distribution of significant variables for each outcome
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
K-means clustering
Results and discussion
Principal Component Analysis
Principal Component Analysis
Principal Component Analysis
K-means clustering
K-means clustering
Results and discussion
- Stage 3 and 4 patients differ in tumor size and acid phosphatase levels
- Most effective treatment is 1.0 mg estrogen
- Significant variables are tumor size, CVD, age, and weight index